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AI, Python, Cognitive Neuroscience

AI, Python, Cognitive Neuroscience

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Post #1771 511
Training on only synthetic data, our new paper gets near the SOTA on the real-world 3DPW human pose dataset by focusing on motion. Simulation is underappreciated for representation learning; you just have to keep the nets from overfitting to the domain.
https://arxiv.org/abs/1907.02499

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Post #1769 532
DeepMind:
For anyone interested in constrained optimisation with DL models (e.g. as in https://arxiv.org/abs/1810.00597 ), we just released a few handy tools to deal with inequality constraints for Sonnet (http://tiny.cc/a9va9y ).

✴️ @AI_Python_EN
arXiv.org Taming VAEs In spite of remarkable progress in deep latent variable generative modeling, training still remains a challenge due to a combination of optimization and generalization issues. In practice, a...
Post #1766 1.03K
Learning from unlabelled videos is key to scaling computer vision models. Our method learns state-of-the-art pose detectors given only examples of skeletons or facial landmarks. It further enables image generation and manipulation conditioned on landmarks.
https://arxiv.org/abs/1907.02055

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Post #1764 606
If you're interested in transfer learning in NLP, you definitely need to check out Daniel Pressel's Github repository. He recently gave a keynote and tutorial at the International Summer School on Deep Learning, 2019 in Gdansk, Poland. The slides are online and really amazing and also he uploaded the tutorial. Colab notebooks are also provided. Check it out! #deeplearning #machinelearning

📊 Slides: https://lnkd.in/dttQJ8P
🔤 Github: https://lnkd.in/d_fscpV

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Post #1763 541
Data at its lowest form of aggregation is not always most useful, and it's very easy to waste time, energy and computing resources trying to model and predict tiny patterns and fluctuations which are, for all intents and purposes, random.

Some big data are unnecessary big, and more value at less cost could have been extracted had the data been intelligently aggregated prior to the modeling.

There is also the habit of using gigantic data files when samples would have sufficed. :-)

✴️ @AI_Python_EN
Post #1762 909
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification. Khaled Koutini, Hamid Eghbal-zadeh, Matthias Dorfer, and Gerhard Widmer
http://arxiv.org/abs/1907.01803

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Post #1760 696
Artificial Intelligence: the global landscape of ethics guidelines

Researchers: Anna Jobin, Marcello Ienca, Effy Vayena
Paper: http://ow.ly/mDA430p2R0q

#artificialintelligence #ai #ml #machinelearning #bigdata #deeplearning #technology #datascience

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Post #1759 705
Facebook today announced the open source release of Deep Learning Recommendation Model (DLRM), a state-of-the-art AI model for serving up personalized results in production environments.

DLRM can be found on GitHub, and implementations of the model are available for Facebook’s PyTorch, Facebook’s distributed learning framework Caffe2, and Glow C++.
Link: https://lnkd.in/dEDtai3

Recommendation engines decide a lot of what people see today, whether it’s content on social media sites like Facebook, ecommerce sites like Amazon, or even the first options you see on an Xbox.

Last month, Amazon made its AI for the shopping recommendations system Personalize available on AWS.
FB Research blog for more details:
https://lnkd.in/du2N9Pd

✴️ @AI_Python_EN
Post #1755 5.31K
Prof. Chris Manning, Director of StanfordAILab & founder of Stanfordnlp, shared inspiring thoughts on research trends and challenges in #computervision and #NLP at #CVPR2019. View full interview:

http://bit.ly/2KR21hO

✴️ @AI_Python_EN
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